With more than 500 million Tweets a day, Twitter has an expansive set of data from which we can glean insights and learn about a variety of topics, from health-related information such as when and where the flu may hit to global events like ringing in the new year. To date, it has been challenging for researchers outside the company who are tackling big questions to collaborate with us to access our public, historical data. Twitter Data Grants program aims to change that by connecting research institutions and academics with the data they need.

1st International Workshop on Scalable Computing For Real-Time Big Data Applications. This workshop aims at providing a venue for designers, practitioners, researchers, developers, and industrial/governmental partners to come together, present and discuss leading research results, use cases, innovative ideas, challenges, and opportunities that arise from real-time big data applications.

What makes a meme— an idea, a phrase, an image—go viral? For starters, the meme must have broad appeal, so it can spread not just within communities of like-minded individuals but can leap from one community to the next. Researchers, by mining public Twitter data, have found that a meme's “virality” is often evident from the start. After only a few dozen tweets, a typical viral meme (as defined by tweets using a given hashtag) will already have caught on in numerous communities of Twitter users. In contrast, a meme destined to peter out will resonate in fewer groups.

Spark, an Apache incubator project, is an open source distributed computing framework for advanced analytics in Hadoop. It's 100X faster than what they are able to achieve with MapReduce. Spark includes a machine learning library (MLLib), a graph engine (GraphX), a streaming analytics engine (Spark Streaming) and much more...

Currently, Spark supports programming interfaces for Scala, Java and Python. The R interface is under development and this is expected to be released in the first half of 2014.

"...We randomly sampled 5,000 numbers from our crowdsourced MetaPhone dataset and queried the Yelp, Google Places, and Facebook directories. With little marginal effort and just those three sources—all free and public—we matched 1,356 (27.1%) of the numbers. Specifically, there were 378 hits (7.6%) on Yelp, 684 (13.7%) on Google Places, and 618 (12.3%) on Facebook..."

We've known big data has had big impacts in business, and in lots of prediction tasks. I want to understand, what does big data mean for what we do for science? Specifically, I want to think about the following context: You have a scientist who has a hypothesis that they would like to test, and I want to think about how the testing of that hypothesis might change as data gets bigger and bigger. So that's going to be the rule of the game. Scientists start with a hypothesis and they want to test it; what's going to happen?

When you think about evolution, 'survival of the fittest' is probably one of the first things that comes into your head. However, new research from Oxford University finds that the 'fittest' may never arrive in the first place and so aren’t around to survive.

This is important news for those of us that work with clients in knowledge intensive scientific industries. Ingesting and analyzing this content will have profound impact on our ability to make connections and see patterns in scientific literature.

Online dating sites have become popular platforms for people to look for potential romantic partners. It is important to understand users' dating preferences in order to make better recommendations on potential dates. The message sending and replying actions of a user are strong indicators for what he/she is looking for in a potential date and reflect the user's actual dating preferences. We study how users' online dating behaviors correlate with various user attributes using a large real-world dateset from a major online dating site in China. Many of our results on user messaging behavior align with notions in social and evolutionary psychology: males tend to look for younger females while females put more emphasis on the socioeconomic status (e.g., income, education level) of a potential date. In addition, we observe that the geographic distance between two users and the photo count of users play an important role in their dating behaviors. Our results show that it is important to differentiate between users' true preferences and random selection. Some user behaviors in choosing attributes in a potential date may largely be a result of random selection. We also find that both males and females are more likely to reply to users whose attributes come closest to the stated preferences of the receivers, and there is significant discrepancy between a user's stated dating preference and his/her actual online dating behavior. These results can provide valuable guidelines to the design of a recommendation engine for potential dates.

By analysing the chemical structure of a drug, researchers can see if it is likely to bind to, or ‘dock’ with, a biological target such as a protein. Researchers have now unveiled a computational effort that used Google's supercomputers to assesses billions of potential dockings on the basis of drug and protein information held in public databases, finding potentially toxic side effects and allowing researchers to predict how and where a compound might work in the body.

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